@article{SR3,
  title={Image super-resolution via iterative refinement},
  author={Saharia, Chitwan and Ho, Jonathan and Chan, William and Salimans, Tim and Fleet, David J and Norouzi, Mohammad},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={45},
  number={4},
  pages={4713--4726},
  year={2022},
  publisher={IEEE}
}
@misc{VAE,
      title={Auto-Encoding Variational Bayes}, 
      author={Diederik P Kingma and Max Welling},
      year={2022},
      eprint={1312.6114},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
@article{kingma2013auto,
  title={Auto-encoding variational bayes},
  author={Kingma, Diederik P and Welling, Max},
  journal={arXiv preprint arXiv:1312.6114},
  year={2013}
}
@misc{srcnn,
      title={Image Super-Resolution Using Deep Convolutional Networks}, 
      author={Chao Dong and Chen Change Loy and Kaiming He and Xiaoou Tang},
      year={2015},
      eprint={1501.00092},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}
@article{srdiff,
  title={Srdiff: Single image super-resolution with diffusion probabilistic models},
  author={Li, Haoying and Yang, Yifan and Chang, Meng and Chen, Shiqi and Feng, Huajun and Xu, Zhihai and Li, Qi and Chen, Yueting},
  journal={Neurocomputing},
  volume={479},
  pages={47--59},
  year={2022},
  publisher={Elsevier}
}

@article{resdiff,
  title={Resdiff: Combining cnn and diffusion model for image super-resolution},
  author={Shang, Shuyao and Shan, Zhengyang and Liu, Guangxing and Zhang, Jinglin},
  journal={arXiv preprint arXiv:2303.08714},
  year={2023}
}
@article{ddpm,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}
@InProceedings{ddpm2,
  title = 	 {Improved Denoising Diffusion Probabilistic Models},
  author =       {Nichol, Alexander Quinn and Dhariwal, Prafulla},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {8162--8171},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf},
  url = 	 {https://proceedings.mlr.press/v139/nichol21a.html},
  abstract = 	 {Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs and GANs cover the target distribution. Finally, we show that the sample quality and likelihood of these models scale smoothly with model capacity and training compute, making them easily scalable. We release our code and pre-trained models at https://github.com/openai/improved-diffusion.}
}
@InProceedings{srgan,
author = {Ledig, Christian and Theis, Lucas and Huszar, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and Shi, Wenzhe},
title = {Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}
@InProceedings{ESRGAN,
author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen},
title = {ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}
@InProceedings{enhanceNet,
author = {Sajjadi, Mehdi S. M. and Scholkopf, Bernhard and Hirsch, Michael},
title = {EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
@InProceedings{at1,
author = {Choi, Jae-Seok and Kim, Munchurl},
title = {A Deep Convolutional Neural Network With Selection Units for Super-Resolution},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
@InProceedings{at2,
author = {Zhang, Yulun and Li, Kunpeng and Li, Kai and Wang, Lichen and Zhong, Bineng and Fu, Yun},
title = {Image Super-Resolution Using Very Deep Residual Channel Attention Networks},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}
@inproceedings{com,
title	= {Image Super-Resolution: A Comprehensive Review},
author	= {Anil Chandra Naidu Matcha},
URL	= {https://arxiv.org/pdf/1706.03762.pdf}
}
@inproceedings{attention,
title	= {Attention is All You Need},
author	= {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
year	= {2017},
URL	= {https://blog.paperspace.com/image-super-resolution/}
}
@inproceedings{img,
title	= {Diffusion Models Made Easy},
author	= {J. Rafid Siddiqui},
URL	={https://towardsdatascience.com/diffusion-models-made-easy-8414298ce4da}
}
@inproceedings{img2,
title	= {An Introduction to Diffusion Models for Machine Learning},
author	= {Akruti Acharya},
URL	={https://encord.com/blog/diffusion-models}
}
@inproceedings{unet,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18},
  pages={234--241},
  year={2015},
  organization={Springer}
}
@article{sx1,
  title={Wavelet frequency separation attention network for chest x-ray image super-resolution},
  author={Yu, Yue and She, Kun and Liu, Jinhua},
  journal={Micromachines},
  volume={12},
  number={11},
  pages={1418},
  year={2021},
  publisher={MDPI}
}
@article{sx2,
  title={Low-dose chest X-ray image super-resolution using generative adversarial nets with spectral normalization},
  author={Xu, Liming and Zeng, Xianhua and Huang, Zhiwei and Li, Weisheng and Zhang, He},
  journal={Biomedical Signal Processing and Control},
  volume={55},
  pages={101600},
  year={2020},
  publisher={Elsevier}
}
@article{sx3,
  title={COVID-19 diagnosis from chest X-ray images using a robust multi-resolution analysis siamese neural network with super-resolution convolutional neural network},
  author={Monday, Happy Nkanta and Li, Jianping and Nneji, Grace Ugochi and Nahar, Saifun and Hossin, Md Altab and Jackson, Jehoiada and Ejiyi, Chukwuebuka Joseph},
  journal={Diagnostics},
  volume={12},
  number={3},
  pages={741},
  year={2022},
  publisher={MDPI}
}
@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
@misc{vinbig,
  title={VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations},
  author={Nguyen, Ha Q and Lam, Khanh and Le, Linh T and Pham, Hieu H and Tran, Dat Q and Nguyen, Dung B and Le, Dung D and Pham, Chi M and Tong, Hang TT and Dinh, Diep H and others},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={429},
  year={2022},
  publisher={Nature Publishing Group UK London}
}
@InProceedings{wang2017chestxray,
author = {Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi
and Summers, Ronald},
title = {ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised
Classification and Localization of Common Thorax Diseases},
booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {3462--3471},
year = {2017}
}
@article{bigGan,
  title={Large scale GAN training for high fidelity natural image synthesis},
  author={Brock, Andrew and Donahue, Jeff and Simonyan, Karen},
  journal={arXiv preprint arXiv:1809.11096},
  year={2018}
}
@inproceedings{yadagiri2023anomaly,
  title={Anomaly Guided Generalizable Super-Resolution of Chest X-Ray Images using Multi-Level Information Rendering},
  author={Yadagiri, Vamshi Vardhan and Reddy, Sekhar and Paul, Angshuman},
  year={2023},
  booktitle={Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention (DGM4MICCAI) @ MICCAI 2023}
}
@misc{artifact,
  howpublished = {\url{https://www.radiologymasterclass.co.uk/gallery/chest/quality/chest-x-ray-artifact}},
}

@misc{brassiere,
  howpublished = {\url{https://radiopaedia.org/cases/brassiere-artifact}},
}

